Producción CyT
Congreso
Autoría
RUBIO SCOLA, IGNACIO EDUARDO JESUS
;
Luis Rodolfo Garcia Carrillo
;
Joao Hespanha
Fecha
2020
Editorial y Lugar de Edición
IEEE
ISSN
978-1-5386-8266-1
Resumen
Información suministrada por el agente en
SIGEVA
In recent years diverse computational models ofemotional learning observed in the mammalian brain haveinspired a number of self-learning control approaches. Thesearchitectures are promising in terms of their learning abilityand low computational cost. However, the lack of rigorousstability analysis and mathematical proofs of stability andperformance has limited the proliferation of these controllers.To address this drawback, this paper proposes a modifiedbrain emotional neural network structure...
In recent years diverse computational models ofemotional learning observed in the mammalian brain haveinspired a number of self-learning control approaches. Thesearchitectures are promising in terms of their learning abilityand low computational cost. However, the lack of rigorousstability analysis and mathematical proofs of stability andperformance has limited the proliferation of these controllers.To address this drawback, this paper proposes a modifiedbrain emotional neural network structure using a radial basisfunction inside the Thalamus and an emotional signal based onan integral action structure to increase performance. Mathe-matical stability proofs are provided, together with numericalsimulations, demonstrating the superior performance obtainedwith the new modifications proposed to the emoional learning-inspired control.
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Palabras Clave
Robust controlLyapunov stabilityEmotional learning